Handwriting & Messy Scans
Read handwriting, faded carbon copies, and photographed receipts with a vision model — where classic OCR returns garbage.
What it is
Vision-model OCR for the documents that defeat classic OCR: handwritten notes, faded carbon-copy forms, receipts photographed at an angle, and dense or unusual layouts. It is the option you reach for when the input is genuinely hard, not pristine.
Why classic OCR fails here
Traditional OCR matches character shapes one glyph at a time. On handwriting or a poor scan it returns confident gibberish and fails silently — you don't find out until the data downstream is already wrong.
What Xberg does
Xberg can run a vision language model that reads the whole page the way a person would — using context to resolve ambiguous strokes and messy layouts. It sits alongside classic OCR and is selectable for the hard inputs.
- Reads handwriting and cursive, not just printed type.
- Recovers faded, low-contrast, and skewed or photographed pages.
- Uses layout and context, so reading order survives complex pages.
- No manual preprocessing or cleanup step to build and babysit.
More use cases
RAG Pipeline Ingestion
Turn a pile of PDFs, Office docs, and HTML into clean, chunked, embedded data for your vector database — in one call.
Document-Reading Agents
Give your AI agents one tool to read any document — 100+ formats, structured output, every framework.
Replace Legacy IDP
Swap brittle, template-based processing for one API that returns schema-mapped JSON — no templates to maintain.